{"id":"W2888773612","doi":"10.1080/15481603.2018.1513444","title":"Monitoring surface changes in discontinuous permafrost terrain using small baseline SAR interferometry, object-based classification, and geological features: a case study from Mayo, Yukon Territory, Canada","year":2018,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"","keywords":"Permafrost; Interferometric synthetic aperture radar; Terrain; Geology; Remote sensing; GNSS augmentation; Land cover; Baseline (sea); Vegetation (pathology); Synthetic aperture radar; Interferometry; Arctic; Subsidence; Physical geography; Geodesy; Cartography; Geomorphology; Geography; Global Positioning System; Land use","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008343011,0.0003114133,0.0003503846,0.0001865331,0.0005851741,0.0002723609,0.0002360898,0.0001078388,0.0001097801],"category_scores_gemma":[0.0001550051,0.0002620821,0.00002942703,0.0005497436,0.0003932976,0.0001729005,0.00006661671,0.000301826,0.000003730763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008546987,"about_ca_system_score_gemma":0.0002139309,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9526593,"about_ca_topic_score_gemma":0.9957827,"domain_scores_codex":[0.9974811,0.0003261879,0.0003526372,0.000810058,0.0003820756,0.0006479518],"domain_scores_gemma":[0.998794,0.0003602809,0.0001538864,0.000364131,0.00009075642,0.0002369756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005392033,0.00002712018,0.9315155,0.00001313139,0.00000613622,0.001796863,0.005754088,0.0003022359,0.02002037,2.82096e-8,0.00002879642,0.04048181],"study_design_scores_gemma":[0.0004341242,0.0002476057,0.5895715,0.0001987228,0.00002353764,0.0006744675,0.03057617,0.376738,0.001046677,0.000004962159,0.00009590458,0.000388296],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969543,0.0002938976,0.0003301361,0.0004652431,0.0008807064,0.0003511639,0.0006142743,0.00003479277,0.00007545874],"genre_scores_gemma":[0.9962393,0.00001660262,0.002766847,0.000249083,0.0005449895,4.798582e-8,0.0001478032,0.00001014139,0.00002517696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3764358,"threshold_uncertainty_score":0.9999831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05946677061716515,"score_gpt":0.2739050385330868,"score_spread":0.2144382679159216,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}